Data Preparation

Import Dataset

Data Preprocess

Defining Preprocess Recipe

Model Fitting

Defining Model Specifications

#> Random Forest Model Specification (classification)
#> 
#> Main Arguments:
#>   mtry = 2
#>   trees = 1500
#>   min_n = 30
#> 
#> Engine-Specific Arguments:
#>   seed = 100
#>   num.threads = parallel::detectCores()
#>   importance = impurity
#> 
#> Computational engine: ranger

Model Fitting

#> parsnip model object
#> 
#> Ranger result
#> 
#> Call:
#>  ranger::ranger(formula = formula, data = data, mtry = ~2, num.trees = ~1500,      min.node.size = ~30, seed = ~100, num.threads = ~parallel::detectCores(),      importance = ~"impurity", verbose = FALSE, probability = TRUE) 
#> 
#> Type:                             Probability estimation 
#> Number of trees:                  1500 
#> Sample size:                      380 
#> Number of independent variables:  30 
#> Mtry:                             2 
#> Target node size:                 30 
#> Variable importance mode:         impurity 
#> Splitrule:                        gini 
#> OOB prediction error (Brier s.):  0.2011784

Model Evaluation

Predict on Test Dataset

Confusion Matrix

ROC Curve

Precision-Recall Curve